Abstract
Previous psychological researches prove that emotion is of importance in individual risk decision making. So could the public emotion (social moods) influence the collective risk decision making such as the stock market? We obtained the five basic social moods including happiness, sadness, fear, anger and disgust by analysing the text content of daily Sina Weibo using our emotion lexicon. Then we investigated the correlation between the daily social moods and Shanghai Composite Index volume by means of Granger causality analysis and linear regression model. We found sadness can significantly improve the predictive accuracy of the trading volume by 2.4% and reduce the MAPE by 8. The analyses for the five social moods indicated the arousal of sadness is the lowest, and the lowest 25% terms of sadness can predict the total trading amounts of SSEC too. The result indicated the negative emotion with lower arousal can improve the tendency of risk taking, which confirms emotional maintenance hypothesis in psychological discipline.
| Original language | English |
|---|---|
| Pages (from-to) | 148-155 |
| Number of pages | 8 |
| Journal | International Journal of Embedded Systems |
| Volume | 7 |
| Issue number | 2 |
| DOIs | |
| State | Published - 1 Jun 2015 |
Keywords
- Arousal
- China
- Emotion lexicon
- Emotional maintenance hypothesis
- Granger causality analysis
- Linear regression model
- Micro-blog
- Risk decision making
- SSEC
- Sadness
- Social mood